Mipy: An Open-Source Framework to improve reproducibility in Brain Microstructure Imaging

International audienc

Noise Floor Removal via Phase Correction of Complex Diffusion-Weighted Images: Influence on DTI and q-space Metrics

International audienceThe non-Gaussian noise distribution in magnitude Diffusion-Weighted Images (DWIs) can severely affect the estimation and reconstruction of the true diffusion signal. As a consequence, also the estimated diffusion metrics can be biased. We study the effect of phase correction, a procedure that re-establishes the Gaussianity of the noise distribution in DWIs by taking into account the corresponding phase images. We quantify the debiasing effects of phase correction in terms of diffusion signal estimation and calculated metrics. We perform in silico experiments based on a MGH Human Connectome Project dataset and on a digital phantom, accounting for different acquisition schemes, diffusion-weightings, signal to noise ratios, and for metrics based on Diffusion Tensor Imaging and on Mean Apparent Propagator Magnetic Resonance Imaging, i.e. q-space metrics. We show that phase correction is still a challenge, but also an effective tool to debias the estimation of diffusion signal and metrics from DWIs, especially at high b-values

Laplacian-Regularized MAP-MRI: Improving Axonal Caliber Estimation

International audienceIn diffusion MRI, the accurate description of the entire diffusion signal from sparse measurements is essential to enable the recovery of microstructural information of the white matter. The recent Mean Apparent Propagator (MAP)-MRI basis is especially well suited for this task, but the basis fitting becomes unreliable in the presence of noise. As a solution we propose a fast and robust analytic Laplacian regularization for MAP-MRI. Using both synthetic diffusion data and human data from the Human Connectome Project we show that (1) MAP-MRI has more accurate microstructure recovery compared to classical techniques, (2) regularized MAP-MRI has lower signal fitting errors compared to the unregularized approach and a positivity constraint on the EAP and (3) that our regularization improves axon radius recovery on human data

Improved Vascular Transport Function Characterization in DSC-MRI via Deconvolution with Dispersion-Compliant Bases

International audienceBolus dispersion affects the residue function computed via deconvolution of DSC-MRI data. The obtained effective residue function can be expressed as the convolution of the true one with a Vascular Transport Function (VTF) that characterizes dispersion. The state-of-the-art technique CPI+VTF allows to estimate the actual residue function by assuming a model of VTF. We propose to perform deconvolution representing the effective residue function with Dispersion-Compliant Bases (DCB) with no assumptions on the VTF, and then apply the CPI+VTF on DCB results, to improve performance

Elucidating Dispersion Effects in Perfusion MRI by Means of Dispersion-Compliant Bases

International audienceDispersion effects in perfusion MRI data have a relevant influence on the residue function computed from deconvolution of the measured arterial and tissular concentration time-curves. Their characterization allows reliable estimation of hemody-namic parameters and can reveal pathological tissue conditions. However, the time-delay between the measured concentration time-curves is a confounding factor. We perform deconvolution by means of dispersion-compliant bases, separating the effects of dispersion and delay. In order to characterize dispersion, we introduce shape parameters, such as the dispersion time and index. We propose a new formulation for the dispersed residue function and perform in silico experiments that validate the reliability of our approach against the block-circulant Singular Value Decomposition. We successfully apply the approach to stroke MRI data and show that the calculated parameters are coherent with physiological considerations, highlighting the importance of dispersion as an effect to be measured rather than discarded

Diffusion MRI Anisotropy: Modeling, Analysis and Interpretation

The micro-architecture of brain tissue obstructs the movement of diffusing water molecules, causing tissue-dependent, often anisotropic diffusion profiles. In diffusion MRI (dMRI), the relation between brain tissue structure and diffusion anisotropy is studied using oriented diffusion gradients, resulting in tissue-and orientation-dependent diffusion-weighted images (DWIs). Over time, various methods have been proposed that summarize these DWIs, that can be measured at different orientations, gradient strengths and diffusion times into one " diffusion anisotropy " measure. This book chapter is dedicated to understanding the similarities and differences between the diffusion anisotropy metrics that different methods estimate. We first discuss the physical interpretation of diffusion anisotropy in terms of the diffusion properties around nervous tissue. We then explain how DWIs are influenced by diffusion anisotropy and the parameters of the dMRI acquisition itself. We then go through the state-of-the-art of signal-based and multi-compartment-based dMRI methods that estimate diffusion anisotropy-related methods, focusing on their limitations and applications. We finally discuss confounding factors in the estimation of diffusion anisotropy and current challenges

Unveiling the Dispersion Kernel in DSC-MRI by Means of Dispersion-Compliant Bases and Control Point Interpolation Techniques

International audienceIn DSC-MRI the presence of dispersion affects the estimation, via deconvolution, of the residue function that characterizes the perfusion in each voxel. Dispersion is descibed by a Vascular Transport Function (VTF) which knolewdge is essential to recover a dispersion-free residue function. State-of-the-art techniques aim at characterizing the VTF but assume a specific shape for it, which in reality is unknown. We propose to estimate the residue function without assumptions by means of Dispersion-Compliant Bases (DCB). We use these results to find which VTF model better describes the in vivo data for each tissue type by means of control point interpolation approaches

Multi-Tissue Multi-Compartment Models of Diffusion MRI

State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package

Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.Comment: 6 pages, 2 figures, 2 table

Multi-compartment modelling of diffusion MRI signal shows TE-based volume fraction bias

International audienceDiffusion MRI (dMRI) has been widely used to estimate brain tissue microstructure in-vivo.Two of the most widely used microstructural indices are the white matter (WM) andintra-cellular (IC) volume fractions (VF) [2012z,2019f]. In estimating these fractions, acommon assumption of dMRI-based signal modeling is to assume that the T2-relaxation foreach compartment is equal. However, it has been shown that this assumption is inaccurate[2018v]. Here, we characterize the bias introduced by this assumption using a generalmulti-compartmental model of the dMRI signal in three distinct scenarios:3-S0) the realistic-case, where each compartment has its T2-dependent signal at b-value 0(S0).2-S0) in which we consider only two separated S0, one for WM and one for CSF similarly to[2014j].1-S0) a single average S0 is considered for all the compartments, as commonly done indMRI.Our simulations and experiments on real data show fitting the WM and IC VF using the moresimplistic 2-S0 and 1-S0 model, a systematic bias appears that potentially alters theinterpretation of conclusions drawn from studies focusing on WM and IC VF